{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"table-detection.ipynb","version":"0.3.2","provenance":[],"collapsed_sections":[]},"kernelspec":{"name":"python3","display_name":"Python 3"},"accelerator":"GPU"},"cells":[{"cell_type":"markdown","metadata":{"id":"8UEDHWlhaHnA","colab_type":"text"},"source":["# Table Detection using Faster RCNN\n","This project aims to perform **Documents Tables Detection** on UNLV dataset using Deep Learning. \n","\n","The proposed methodology for carrying out Table Detection in Documents consists of Faster Region based Convolutional Neural Network (Faster RCNN) for detection as its basic element. Faster RCNN is further divided into several layers where each layer contribute to detection. Faster RCNN is composed of following layers:\n","\n","- Feature Extraction Layer to extract features from images\n","- Region Proposal Network (RPN) Layer that gives proposals for different set of regions in an image\n","- Region of Interest Layer that filters out from the proposed regions based on threshold\n","\n","Following are the libraries that we will import."]},{"cell_type":"code","metadata":{"id":"tFYVmLWwZ_oJ","colab_type":"code","colab":{}},"source":["# Import Libraries\n","!pip install tensorflow-gpu==2.0.0alpha0\n","import pandas as pd\n","import os\n","import cv2\n","import tensorflow as tf\n","from tensorflow.keras import losses\n","import tensorflow.keras.backend as K\n","import numpy as np\n","import matplotlib.pyplot as plt\n","from __future__ import print_function\n","import math\n","import random\n","from keras.utils.generic_utils import Progbar\n","from keras.backend import image_dim_ordering\n","import copy"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"QkZI5slkaPXN","colab_type":"text"},"source":["## Download Data\n","First, we need the data. For this project, we will download and extract the **UNLV dataset**. The dataset can be downloaded from the link [here](https://drive.google.com/file/d/1ETq5hhoIgCzzom6yivkokhQ8DoOm6nDs/view?usp=sharing). "]},{"cell_type":"code","metadata":{"id":"XTf7oxqHaNww","colab_type":"code","colab":{}},"source":["# Download data from local repository\n","from google.colab import drive\n","drive.mount('/content/gdrive')\n","%cd /content\n","!mkdir data_fasterrcnn\n","!cp -r \"/content/gdrive/My Drive/Google Colab Workspace/Table Detection using Faster RCNN/data.zip\" \"/content/data_fasterrcnn/\""],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"lUCvC4XThUyL","colab_type":"code","colab":{}},"source":["# Unzip data\n","!unzip /content/data_fasterrcnn/data -d /content/data_fasterrcnn/"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"h0h6Pa7UFkaH","colab_type":"text"},"source":["## Data Preprocessing"]},{"cell_type":"markdown","metadata":{"id":"85QBDz8raaIE","colab_type":"text"},"source":["### Load Ground Truth CSV Files\n","After downloading the dataset, we can see that there is an `images` folder and two csv files i.e. `train.csv` and `test.csv`. The next step is to load both the csv files into pandas dataframe for easy indexing.\n","\n","The code below loads the csv files into pandas dataframe."]},{"cell_type":"code","metadata":{"id":"KTNkba33iymp","colab_type":"code","outputId":"1156de45-981b-40e1-d82f-866c0dd8f8a5","executionInfo":{"status":"ok","timestamp":1562753283252,"user_tz":-300,"elapsed":1241,"user":{"displayName":"Maliha Sameen","photoUrl":"","userId":"02324899299454165728"}},"colab":{"base_uri":"https://localhost:8080/","height":459}},"source":["# Load CSV Files into DataFrames\n","train_file = pd.read_csv('/content/data_fasterrcnn/train.csv',names=['image_names','xmin','ymin','xmax','ymax','class'])\n","test_file = pd.read_csv('/content/data_fasterrcnn/val.csv',names=['image_names','xmin','ymin','xmax','ymax','class'])\n","\n","# Display DataFrames\n","print(\"\\nTraining Ground Truth\")\n","display(train_file.head())\n","print(\"\\nTesting Ground Truth\")\n","display(test_file.head())"],"execution_count":256,"outputs":[{"output_type":"stream","text":["\n","Training Ground Truth\n"],"name":"stdout"},{"output_type":"display_data","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>image_names</th>\n","      <th>xmin</th>\n","      <th>ymin</th>\n","      <th>xmax</th>\n","      <th>ymax</th>\n","      <th>class</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>0101_003.png</td>\n","      <td>770</td>\n","      <td>946</td>\n","      <td>2070</td>\n","      <td>2973</td>\n","      <td>table</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>0110_099.png</td>\n","      <td>270</td>\n","      <td>1653</td>\n","      <td>2280</td>\n","      <td>2580</td>\n","      <td>table</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>0113_013.png</td>\n","      <td>303</td>\n","      <td>343</td>\n","      <td>2273</td>\n","      <td>2953</td>\n","      <td>table</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>0140_007.png</td>\n","      <td>664</td>\n","      <td>1782</td>\n","      <td>1814</td>\n","      <td>2076</td>\n","      <td>table</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>0146_281.png</td>\n","      <td>704</td>\n","      <td>432</td>\n","      <td>1744</td>\n","      <td>1552</td>\n","      <td>table</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["    image_names  xmin  ymin  xmax  ymax  class\n","0  0101_003.png   770   946  2070  2973  table\n","1  0110_099.png   270  1653  2280  2580  table\n","2  0113_013.png   303   343  2273  2953  table\n","3  0140_007.png   664  1782  1814  2076  table\n","4  0146_281.png   704   432  1744  1552  table"]},"metadata":{"tags":[]}},{"output_type":"stream","text":["\n","Testing Ground Truth\n"],"name":"stdout"},{"output_type":"display_data","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>image_names</th>\n","      <th>xmin</th>\n","      <th>ymin</th>\n","      <th>xmax</th>\n","      <th>ymax</th>\n","      <th>class</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>9533_039.png</td>\n","      <td>60</td>\n","      <td>396</td>\n","      <td>1113</td>\n","      <td>2420</td>\n","      <td>table</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>9533_039.png</td>\n","      <td>1143</td>\n","      <td>1126</td>\n","      <td>2240</td>\n","      <td>2230</td>\n","      <td>table</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>9534_001.png</td>\n","      <td>196</td>\n","      <td>378</td>\n","      <td>2146</td>\n","      <td>956</td>\n","      <td>table</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>9534_001.png</td>\n","      <td>184</td>\n","      <td>1028</td>\n","      <td>2160</td>\n","      <td>1636</td>\n","      <td>table</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>9534_028.png</td>\n","      <td>642</td>\n","      <td>1388</td>\n","      <td>1944</td>\n","      <td>1981</td>\n","      <td>table</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["    image_names  xmin  ymin  xmax  ymax  class\n","0  9533_039.png    60   396  1113  2420  table\n","1  9533_039.png  1143  1126  2240  2230  table\n","2  9534_001.png   196   378  2146   956  table\n","3  9534_001.png   184  1028  2160  1636  table\n","4  9534_028.png   642  1388  1944  1981  table"]},"metadata":{"tags":[]}}]},{"cell_type":"markdown","metadata":{"id":"XJ4eOW7yL7uM","colab_type":"text"},"source":["### Resize Images and Bbox Coordinates\n","As the original images are much larger in size such that the smallest width and the smallest height is 2544px.  This may cause memory overflow during training. Also the images are all not of same size, so we will resize the images to 256 x 256 pixels and resize the bounding box coordinates according to it.\n","\n","Following code resize the images and bounding box coordinates."]},{"cell_type":"code","metadata":{"id":"DuvhLqMAlIxl","colab_type":"code","colab":{}},"source":["def data_loading_and_resizing(image_file):\n","  image_list = image_file['image_names'].unique()\n","  images = []\n","  labels = []\n","  for image_name in image_list:\n","    image = cv2.imread('/content/data_fasterrcnn/images/'+image_name)\n","\n","    x_scale = 256 / image.shape[1]\n","    y_scale = 256 / image.shape[0]\n","    \n","    # Resize Images to 256px x 256px\n","    image = cv2.resize(image, (256,256)) \n","    \n","    # Resize the bbox coordinates\n","    image_file.loc[image_file['image_names'] == image_name,'xmin'] = np.round(image_file.loc[image_file['image_names'] == image_name,'xmin'] * x_scale)\n","    image_file.loc[image_file['image_names'] == image_name,'ymin'] = np.round(image_file.loc[image_file['image_names'] == image_name,'ymin'] * y_scale)\n","    image_file.loc[image_file['image_names'] == image_name,'xmax'] = np.round(image_file.loc[image_file['image_names'] == image_name,'xmax'] * x_scale)\n","    image_file.loc[image_file['image_names'] == image_name,'ymax'] = np.round(image_file.loc[image_file['image_names'] == image_name,'ymax'] * y_scale)\n","  \n","    bboxes = image_file.loc[image_file['image_names'] == image_name,'xmin':'ymax'].values.astype(np.int32)\n","    labels.append(bboxes)\n","\n","    images.append(image)\n","  \n","  return images, labels, image_file"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"oZiqRPCADCFP","colab_type":"code","outputId":"1727bf6f-59ef-45dc-b71b-1bc46cc189d0","executionInfo":{"status":"ok","timestamp":1562753317355,"user_tz":-300,"elapsed":25543,"user":{"displayName":"Maliha Sameen","photoUrl":"","userId":"02324899299454165728"}},"colab":{"base_uri":"https://localhost:8080/","height":459}},"source":["# Resize and Load Data\n","train_images, train_labels, train_file = data_loading_and_resizing(train_file)\n","test_images, test_labels, test_file = data_loading_and_resizing(test_file)\n","\n","# Display DataFrames\n","print(\"\\nResized Training Ground Truth\")\n","display(train_file.head())\n","print(\"\\nResized Testing Ground Truth\")\n","display(test_file.head())"],"execution_count":257,"outputs":[{"output_type":"stream","text":["\n","Resized Training Ground Truth\n"],"name":"stdout"},{"output_type":"display_data","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>image_names</th>\n","      <th>xmin</th>\n","      <th>ymin</th>\n","      <th>xmax</th>\n","      <th>ymax</th>\n","      <th>class</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>0101_003.png</td>\n","      <td>77.0</td>\n","      <td>73.0</td>\n","      <td>208.0</td>\n","      <td>231.0</td>\n","      <td>table</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>0110_099.png</td>\n","      <td>27.0</td>\n","      <td>128.0</td>\n","      <td>229.0</td>\n","      <td>200.0</td>\n","      <td>table</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>0113_013.png</td>\n","      <td>30.0</td>\n","      <td>27.0</td>\n","      <td>229.0</td>\n","      <td>229.0</td>\n","      <td>table</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>0140_007.png</td>\n","      <td>66.0</td>\n","      <td>138.0</td>\n","      <td>181.0</td>\n","      <td>161.0</td>\n","      <td>table</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>0146_281.png</td>\n","      <td>71.0</td>\n","      <td>34.0</td>\n","      <td>175.0</td>\n","      <td>120.0</td>\n","      <td>table</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["    image_names  xmin   ymin   xmax   ymax  class\n","0  0101_003.png  77.0   73.0  208.0  231.0  table\n","1  0110_099.png  27.0  128.0  229.0  200.0  table\n","2  0113_013.png  30.0   27.0  229.0  229.0  table\n","3  0140_007.png  66.0  138.0  181.0  161.0  table\n","4  0146_281.png  71.0   34.0  175.0  120.0  table"]},"metadata":{"tags":[]}},{"output_type":"stream","text":["\n","Resized Testing Ground Truth\n"],"name":"stdout"},{"output_type":"display_data","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>image_names</th>\n","      <th>xmin</th>\n","      <th>ymin</th>\n","      <th>xmax</th>\n","      <th>ymax</th>\n","      <th>class</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>9533_039.png</td>\n","      <td>6.0</td>\n","      <td>31.0</td>\n","      <td>112.0</td>\n","      <td>188.0</td>\n","      <td>table</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>9533_039.png</td>\n","      <td>115.0</td>\n","      <td>87.0</td>\n","      <td>225.0</td>\n","      <td>173.0</td>\n","      <td>table</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>9534_001.png</td>\n","      <td>20.0</td>\n","      <td>29.0</td>\n","      <td>215.0</td>\n","      <td>74.0</td>\n","      <td>table</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>9534_001.png</td>\n","      <td>18.0</td>\n","      <td>80.0</td>\n","      <td>217.0</td>\n","      <td>127.0</td>\n","      <td>table</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>9534_028.png</td>\n","      <td>64.0</td>\n","      <td>108.0</td>\n","      <td>195.0</td>\n","      <td>154.0</td>\n","      <td>table</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["    image_names   xmin   ymin   xmax   ymax  class\n","0  9533_039.png    6.0   31.0  112.0  188.0  table\n","1  9533_039.png  115.0   87.0  225.0  173.0  table\n","2  9534_001.png   20.0   29.0  215.0   74.0  table\n","3  9534_001.png   18.0   80.0  217.0  127.0  table\n","4  9534_028.png   64.0  108.0  195.0  154.0  table"]},"metadata":{"tags":[]}}]},{"cell_type":"markdown","metadata":{"id":"ERfaQdvvf1fO","colab_type":"text"},"source":["### Data Visualization\n","Before generating the ground truth anchors, the most important task is to visualize the training and testing dataset."]},{"cell_type":"code","metadata":{"id":"BMpAo9aVrJr_","colab_type":"code","colab":{}},"source":["def visualize(index_list, images, labels):\n","  rows = 2\n","  columns = 3\n","  fig=plt.figure(figsize=(16, 12))\n","  for i, index in enumerate(index_list):\n","    fig.add_subplot(rows, columns, i+1, xticks=[], yticks=[])\n","    image = images[index]\n","    for label in labels[index]:\n","      cv2.rectangle(image,(label[0], label[1]),(label[2],label[3]),(255, 0, 0))\n","    plt.imshow(image)\n","  plt.show()"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"UbG79Y1BxghE","colab_type":"code","outputId":"e4cf3aa2-3fe3-4ac2-f5cf-fb9ae8c2f6db","executionInfo":{"status":"ok","timestamp":1562753451960,"user_tz":-300,"elapsed":2440,"user":{"displayName":"Maliha Sameen","photoUrl":"","userId":"02324899299454165728"}},"colab":{"base_uri":"https://localhost:8080/","height":673}},"source":["print('Train Image Dataset')\n","visualize([1,5,10,15,20,25], np.copy(train_images), train_labels)"],"execution_count":262,"outputs":[{"output_type":"stream","text":["Train Image Dataset\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAA5IAAAJ/CAYAAAAK3Oz6AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nXrNrS7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KNIJ4zLVmyBBdddJFnuRcvXoyLL7446XxM04zajTSV+y6etKKWK4D1mhfDMFYJ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\nAADEQiAJAAAAAIiFQBIAAAAAEAuBJAAAAAAgFgJJAAAAAEAsBJIAAA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hRI0nSJ598ot27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\nAAAAAElFTkSuQmCC\n","text/plain":["<Figure size 1152x864 with 6 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"Ea51lHTVxg8j","colab_type":"code","outputId":"69e19a4d-1552-486f-a16c-e8da2093cab3","executionInfo":{"status":"ok","timestamp":1562753485324,"user_tz":-300,"elapsed":2683,"user":{"displayName":"Maliha Sameen","photoUrl":"","userId":"02324899299454165728"}},"colab":{"base_uri":"https://localhost:8080/","height":673}},"source":["print('Test Image Dataset')\n","visualize([1,5,10,15,20,25], np.copy(test_images), test_labels)"],"execution_count":264,"outputs":[{"output_type":"stream","text":["Test Image Dataset\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAA5IAAAJ/CAYAAAAK3Oz6AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAOrZq7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ysLOXLS2TzE71+/bo6H4KCguzqTEQ0fPhwIrJNpZXf/yto1HUj3Ne5QQHuYdrz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\nyszMVLKM+sp2A4a5e2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uvviojIyON15WamuqYU2k/Z2hoqBRCGGlxcXFSSinHjBkjw8PD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\nZgq8soTI4ekAAAAASUVORK5CYII=\n","text/plain":["<Figure size 1152x864 with 6 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"markdown","metadata":{"id":"yryTAYYzw_wZ","colab_type":"text"},"source":["### Generate Ground Truth Anchors\n","From above visualization we can see that the number of tables in a document image vary and therefore we cannot pass the bbox coordinates as ground truth in tensorflow model. In order to solve the variable length problem we will generate fixed number of anchors that serve as reference bounding boxes.\n","\n","Following code generates fixed number of ground truth anchors for images."]},{"cell_type":"code","metadata":{"id":"pJPgIpcTUu32","colab_type":"code","colab":{}},"source":["def union(au, bu, area_intersection):\n","\tarea_a = (au[2] - au[0]) * (au[3] - au[1])\n","\tarea_b = (bu[2] - bu[0]) * (bu[3] - bu[1])\n","\tarea_union = area_a + area_b - area_intersection\n","\treturn area_union\n","\n","\n","def intersection(ai, bi):\n","\tx = max(ai[0], bi[0])\n","\ty = max(ai[1], bi[1])\n","\tw = min(ai[2], bi[2]) - x\n","\th = min(ai[3], bi[3]) - y\n","\tif w < 0 or h < 0:\n","\t\treturn 0\n","\treturn w*h\n","\n","\n","def iou(a, b):\n","\t# a and b should be (x1,y1,x2,y2)\n","\n","\tif a[0] >= a[2] or a[1] >= a[3] or b[0] >= b[2] or b[1] >= b[3]:\n","\t\treturn 0.0\n","\n","\tarea_i = intersection(a, b)\n","\tarea_u = union(a, b, area_i)\n","\n","\treturn float(area_i) / float(area_u + 1e-6)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"aSdxuKhC65aS","colab_type":"code","colab":{}},"source":["def calc_rpn(image_data, labels, width, height, resized_width, resized_height):\n","  \n","  downscale = float(16)\n","  anchor_sizes = [64, 128, 256]\n","  anchor_ratios = [[1, 1], [1./math.sqrt(2), 2./math.sqrt(2)], [2./math.sqrt(2), 1./math.sqrt(2)]]  # 1:1, 1:2*sqrt(2), 2*sqrt(2):1\n","  num_anchors = 9\n","  \n","  output_width = resized_width // 16\n","  output_height = resized_height // 16\n","  \n","  n_anchratios = len(anchor_ratios)\n","  \n","  num_bboxes = len(labels)\n","  \n","  y_rpn_overlap = np.zeros((output_height, output_width, num_anchors))\n","  y_is_box_valid = np.zeros((output_height, output_width, num_anchors))\n","  y_rpn_regr = np.zeros((output_height, output_width, num_anchors * 4))\n","  \n","  num_anchors_for_bbox = np.zeros(num_bboxes).astype(int)\n","  best_anchor_for_bbox = -1 * np.ones((num_bboxes, 4)).astype(int)\n","  best_iou_for_bbox = np.zeros(num_bboxes).astype(np.float32)\n","  best_x_for_bbox = np.zeros((num_bboxes, 4)).astype(int)\n","  best_dx_for_bbox = np.zeros((num_bboxes, 4)).astype(np.float32)\n","  \n","  gta = np.zeros((num_bboxes, 4))\n","  for bbox_num, label in enumerate(labels):\n","\t\t# get the GT box coordinates, and resize to account for image resizing\n","    gta[bbox_num, 0] = label[0]\n","    gta[bbox_num, 1] = label[1]\n","    gta[bbox_num, 2] = label[2]\n","    gta[bbox_num, 3] = label[3]\n","  \n","  for anchor_size_idx in range(len(anchor_sizes)):\n","    for anchor_ratio_idx in range(n_anchratios):\n","      anchor_x = anchor_sizes[anchor_size_idx] * anchor_ratios[anchor_ratio_idx][0]\n","      anchor_y = anchor_sizes[anchor_size_idx] * anchor_ratios[anchor_ratio_idx][1]\n","      \n","      for ix in range(output_width):\t\t\t\t\t\n","\t\t\t\t# x-coordinates of the current anchor box\n","        x1_anc = downscale * (ix + 0.5) - anchor_x / 2\n","        x2_anc = downscale * (ix + 0.5) + anchor_x / 2\t\n","\t\t\t\t\n","\t\t\t\t# ignore boxes that go across image boundaries\n","        if x1_anc < 0 or x2_anc > resized_width:\n","          continue\n","        \n","        for jy in range(output_height):\n","\n","\t\t\t\t\t# y-coordinates of the current anchor box\n","          y1_anc = downscale * (jy + 0.5) - anchor_y / 2\n","          y2_anc = downscale * (jy + 0.5) + anchor_y / 2\n","\n","\t\t\t\t\t# ignore boxes that go across image boundaries\n","          if y1_anc < 0 or y2_anc > resized_height:\n","            continue\n","\n","\t\t\t\t\t# bbox_type indicates whether an anchor should be a target\n","\t\t\t\t\t# Initialize with 'negative'\n","          bbox_type = 'neg'\n","\n","\t\t\t\t\t# this is the best IOU for the (x,y) coord and the current anchor\n","\t\t\t\t\t# note that this is different from the best IOU for a GT bbox\n","          best_iou_for_loc = 0.0\n","          \n","          for bbox_num in range(num_bboxes):\n","\t\t\t\t\t\t\n","\t\t\t\t\t\t# get IOU of the current GT box and the current anchor box\n","            curr_iou = iou([gta[bbox_num, 0], gta[bbox_num, 2], gta[bbox_num, 1], gta[bbox_num, 3]], [x1_anc, y1_anc, x2_anc, y2_anc])\n","\t\t\t\t\t\t# calculate the regression targets if they will be needed\n","            if curr_iou > best_iou_for_bbox[bbox_num] or curr_iou > 0.7:\n","              cx = (gta[bbox_num, 0] + gta[bbox_num, 1]) / 2.0\n","              cy = (gta[bbox_num, 2] + gta[bbox_num, 3]) / 2.0\n","              cxa = (x1_anc + x2_anc)/2.0\n","              cya = (y1_anc + y2_anc)/2.0\n","\n","\t\t\t\t\t\t\t# x,y are the center point of ground-truth bbox\n","\t\t\t\t\t\t\t# xa,ya are the center point of anchor bbox (xa=downscale * (ix + 0.5); ya=downscale * (iy+0.5))\n","\t\t\t\t\t\t\t# w,h are the width and height of ground-truth bbox\n","\t\t\t\t\t\t\t# wa,ha are the width and height of anchor bboxe\n","\t\t\t\t\t\t\t# tx = (x - xa) / wa\n","\t\t\t\t\t\t\t# ty = (y - ya) / ha\n","\t\t\t\t\t\t\t# tw = log(w / wa)\n","\t\t\t\t\t\t\t# th = log(h / ha)\n","              tx = (cx - cxa) / (x2_anc - x1_anc)\n","              ty = (cy - cya) / (y2_anc - y1_anc)\n","              tw = np.log((gta[bbox_num, 1] - gta[bbox_num, 0]) / (x2_anc - x1_anc))\n","              th = np.log((gta[bbox_num, 3] - gta[bbox_num, 2]) / (y2_anc - y1_anc))\n","            \n","            #if image_data['bboxes'][bbox_num]['class'] != 'bg':\n","\n","            # all GT boxes should be mapped to an anchor box, so we keep track of which anchor box was best\n","            if curr_iou > best_iou_for_bbox[bbox_num]:\n","              best_anchor_for_bbox[bbox_num] = [jy, ix, anchor_ratio_idx, anchor_size_idx]\n","              best_iou_for_bbox[bbox_num] = curr_iou\n","              best_x_for_bbox[bbox_num,:] = [x1_anc, x2_anc, y1_anc, y2_anc]\n","              best_dx_for_bbox[bbox_num,:] = [tx, ty, tw, th]\n","\n","            # we set the anchor to positive if the IOU is >0.7 (it does not matter if there was another better box, it just indicates overlap)\n","            if curr_iou > 0.7:\n","              bbox_type = 'pos'\n","              num_anchors_for_bbox[bbox_num] += 1\n","              # we update the regression layer target if this IOU is the best for the current (x,y) and anchor position\n","              if curr_iou > best_iou_for_loc:\n","                best_iou_for_loc = curr_iou\n","                best_regr = (tx, ty, tw, th)\n","\n","            # if the IOU is >0.3 and <0.7, it is ambiguous and no included in the objective\n","            if 0.3 < curr_iou < 0.7:\n","              # gray zone between neg and pos\n","              if bbox_type != 'pos':\n","                bbox_type = 'neutral'\n","\n","\t\t\t\t\t# turn on or off outputs depending on IOUs\n","          if bbox_type == 'neg':\n","            y_is_box_valid[jy, ix, anchor_ratio_idx + n_anchratios * anchor_size_idx] = 1\n","            y_rpn_overlap[jy, ix, anchor_ratio_idx + n_anchratios * anchor_size_idx] = 0\n","          elif bbox_type == 'neutral':\n","            y_is_box_valid[jy, ix, anchor_ratio_idx + n_anchratios * anchor_size_idx] = 0\n","            y_rpn_overlap[jy, ix, anchor_ratio_idx + n_anchratios * anchor_size_idx] = 0\n","          elif bbox_type == 'pos':\n","            y_is_box_valid[jy, ix, anchor_ratio_idx + n_anchratios * anchor_size_idx] = 1\n","            y_rpn_overlap[jy, ix, anchor_ratio_idx + n_anchratios * anchor_size_idx] = 1\n","            start = 4 * (anchor_ratio_idx + n_anchratios * anchor_size_idx)\n","            y_rpn_regr[jy, ix, start:start+4] = best_regr\n","\n","\t# we ensure that every bbox has at least one positive RPN region\n","  for idx in range(num_anchors_for_bbox.shape[0]):\n","    if num_anchors_for_bbox[idx] == 0:\n","\t\t\t# no box with an IOU greater than zero ...\n","      if best_anchor_for_bbox[idx, 0] == -1:\n","        continue\n","      y_is_box_valid[best_anchor_for_bbox[idx,0], best_anchor_for_bbox[idx,1], best_anchor_for_bbox[idx,2] + n_anchratios * best_anchor_for_bbox[idx,3]] = 1\n","      y_rpn_overlap[best_anchor_for_bbox[idx,0], best_anchor_for_bbox[idx,1], best_anchor_for_bbox[idx,2] + n_anchratios * best_anchor_for_bbox[idx,3]] = 1\n","      start = 4 * (best_anchor_for_bbox[idx,2] + n_anchratios * best_anchor_for_bbox[idx,3])\n","      y_rpn_regr[best_anchor_for_bbox[idx,0], best_anchor_for_bbox[idx,1], start:start+4] = best_dx_for_bbox[idx, :]\n","  \n","  y_rpn_overlap = np.transpose(y_rpn_overlap, (2, 0, 1))\n","  y_rpn_overlap = np.expand_dims(y_rpn_overlap, axis=0)\n","  \n","  y_is_box_valid = np.transpose(y_is_box_valid, (2, 0, 1))\n","  y_is_box_valid = np.expand_dims(y_is_box_valid, axis=0)\n","  \n","  y_rpn_regr = np.transpose(y_rpn_regr, (2, 0, 1))\n","  y_rpn_regr = np.expand_dims(y_rpn_regr, axis=0)\n","  \n","  pos_locs = np.where(np.logical_and(y_rpn_overlap[0, :, :, :] == 1, y_is_box_valid[0, :, :, :] == 1))\n","  neg_locs = np.where(np.logical_and(y_rpn_overlap[0, :, :, :] == 0, y_is_box_valid[0, :, :, :] == 1))\n","  \n","  num_pos = len(pos_locs[0])\n","\n","\t# one issue is that the RPN has many more negative than positive regions, so we turn off some of the negative\n","\t# regions. We also limit it to 256 regions.\n","  num_regions = 256\n","  \n","  if len(pos_locs[0]) > num_regions/2:\n","    val_locs = random.sample(range(len(pos_locs[0])), len(pos_locs[0]) - num_regions/2)\n","    y_is_box_valid[0, pos_locs[0][val_locs], pos_locs[1][val_locs], pos_locs[2][val_locs]] = 0\n","    num_pos = num_regions/2\n","  \n","  if len(neg_locs[0]) + num_pos > num_regions:\n","    val_locs = random.sample(range(len(neg_locs[0])), len(neg_locs[0]) - num_pos)\n","    y_is_box_valid[0, neg_locs[0][val_locs], neg_locs[1][val_locs], neg_locs[2][val_locs]] = 0\n","  \n","  y_rpn_cls = np.concatenate([y_is_box_valid, y_rpn_overlap], axis=1)\n","  y_rpn_regr = np.concatenate([np.repeat(y_rpn_overlap, 4, axis=1), y_rpn_regr], axis=1)\n","  \n","  return np.copy(y_rpn_cls), np.copy(y_rpn_regr), num_pos"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"Rrgh5HKOxH0j","colab_type":"code","colab":{}},"source":["def get_anchor_gt(images, labels):\n","  resized_width = 256\n","  resized_height = 256\n","  while True:\n","    for index in range(len(images)):\n","      image_data = images[index]\n","      label_data = labels[index]\n","      try:\n","\n","        try:\n","          y_rpn_cls, y_rpn_regr, num_pos = calc_rpn(image_data, label_data, 256, 256, resized_width, resized_height)\n","        except:\n","          continue\n","\n","        # Zero-center by mean pixel, and preprocess image\n","\n","        image_data = image_data[:,:, (2, 1, 0)]  # BGR -> RGB\n","        image_data = image_data.astype(np.float32)\n","        image_data[:, :, 0] -= 103.939\n","        image_data[:, :, 1] -= 116.779\n","        image_data[:, :, 2] -= 123.68\n","        image_data /= 1.0\n","\n","        image_data = np.transpose(image_data, (2, 0, 1))\n","        image_data = np.expand_dims(image_data, axis=0)\n","\n","        y_rpn_regr[:, y_rpn_regr.shape[1]//2:, :, :] *= 1.0\n","\n","        image_data = np.transpose(image_data, (0, 2, 3, 1))\n","        y_rpn_cls = np.transpose(y_rpn_cls, (0, 2, 3, 1))\n","        y_rpn_regr = np.transpose(y_rpn_regr, (0, 2, 3, 1))\n","\n","        yield np.copy(image_data), [np.copy(y_rpn_cls), np.copy(y_rpn_regr)], label_data, index\n","\n","      except Exception as e:\n","        print(e)\n","        continue"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"vYvY66QXdANe","colab_type":"text"},"source":["## Define Model Layers\n","The model that is implemented in this project consists of following layers:\n","\n","- Feature Extraction Layer to extract features from images\n","- Region Proposal Network (RPN) Layer that gives proposals for different set of regions in an image\n","- RPN to Region of Interest (RoI) Layer that convert the rpn proposals into different regions.\n","- Region of Interest Layer that filters out from the proposed regions based on threshold.\n"]},{"cell_type":"markdown","metadata":{"id":"iMau9BQXGxp3","colab_type":"text"},"source":["### Feature Extraction Layer\n","The first step in our model is to use any CNN based model to extract features from the images. For this project we will use **VGG16** as the base model, pretrained on **ImageNet**, for extracting features from the images. We will use the output of second last layer before max pooling and send it to the Region Proposal Network Layer."]},{"cell_type":"code","metadata":{"id":"z3pKKO4XcjRr","colab_type":"code","outputId":"43a9f4b6-0a81-4d71-eebf-c23a4d9058dd","executionInfo":{"status":"ok","timestamp":1562740559385,"user_tz":-300,"elapsed":5314,"user":{"displayName":"Maliha Sameen","photoUrl":"","userId":"02324899299454165728"}},"colab":{"base_uri":"https://localhost:8080/","height":51}},"source":["IMG_SHAPE = (256, 256, 3)\n","base_model = tf.keras.applications.vgg16.VGG16(input_shape=IMG_SHAPE,\n","                                               include_top=False,\n","                                               weights='imagenet')\n","bottleneck_input  = base_model.get_layer(index=0).input\n","bottleneck_output = base_model.get_layer(index=-2).output\n","base_model  = tf.keras.models.Model(inputs=bottleneck_input, outputs=bottleneck_output)"],"execution_count":13,"outputs":[{"output_type":"stream","text":["Downloading data from https://github.com/fchollet/deep-learning-models/releases/download/v0.1/vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5\n","58892288/58889256 [==============================] - 3s 0us/step\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"aqhv0mS1gar8","colab_type":"code","outputId":"c376bcb4-115c-4ae2-bf16-a903a113000f","executionInfo":{"status":"ok","timestamp":1562740578797,"user_tz":-300,"elapsed":1241,"user":{"displayName":"Maliha Sameen","photoUrl":"","userId":"02324899299454165728"}},"colab":{"base_uri":"https://localhost:8080/","height":765}},"source":["base_model.summary()"],"execution_count":14,"outputs":[{"output_type":"stream","text":["Model: \"model\"\n","_________________________________________________________________\n","Layer (type)                 Output Shape              Param #   \n","=================================================================\n","input_1 (InputLayer)         [(None, 256, 256, 3)]     0         \n","_________________________________________________________________\n","block1_conv1 (Conv2D)        (None, 256, 256, 64)      1792      \n","_________________________________________________________________\n","block1_conv2 (Conv2D)        (None, 256, 256, 64)      36928     \n","_________________________________________________________________\n","block1_pool (MaxPooling2D)   (None, 128, 128, 64)      0         \n","_________________________________________________________________\n","block2_conv1 (Conv2D)        (None, 128, 128, 128)     73856     \n","_________________________________________________________________\n","block2_conv2 (Conv2D)        (None, 128, 128, 128)     147584    \n","_________________________________________________________________\n","block2_pool (MaxPooling2D)   (None, 64, 64, 128)       0         \n","_________________________________________________________________\n","block3_conv1 (Conv2D)        (None, 64, 64, 256)       295168    \n","_________________________________________________________________\n","block3_conv2 (Conv2D)        (None, 64, 64, 256)       590080    \n","_________________________________________________________________\n","block3_conv3 (Conv2D)        (None, 64, 64, 256)       590080    \n","_________________________________________________________________\n","block3_pool (MaxPooling2D)   (None, 32, 32, 256)       0         \n","_________________________________________________________________\n","block4_conv1 (Conv2D)        (None, 32, 32, 512)       1180160   \n","_________________________________________________________________\n","block4_conv2 (Conv2D)        (None, 32, 32, 512)       2359808   \n","_________________________________________________________________\n","block4_conv3 (Conv2D)        (None, 32, 32, 512)       2359808   \n","_________________________________________________________________\n","block4_pool (MaxPooling2D)   (None, 16, 16, 512)       0         \n","_________________________________________________________________\n","block5_conv1 (Conv2D)        (None, 16, 16, 512)       2359808   \n","_________________________________________________________________\n","block5_conv2 (Conv2D)        (None, 16, 16, 512)       2359808   \n","_________________________________________________________________\n","block5_conv3 (Conv2D)        (None, 16, 16, 512)       2359808   \n","=================================================================\n","Total params: 14,714,688\n","Trainable params: 14,714,688\n","Non-trainable params: 0\n","_________________________________________________________________\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"jo8dB7ILpOT6","colab_type":"text"},"source":["### Region Proposel Network Layer\n","Using the features extracted by pretrained VGG16, RPN is used to find upto a predefined number of regions (bounding boxes) which contains objects. RPN takes the number of anchors as an input and output a set of good proposals. RPN consists of a convolution layer, classification layer and a regression layer. For each anchor, RPN outputs an objectness score, that we get from the classification layer, and a set of bounding box coordinates that we get from the regression layer."]},{"cell_type":"code","metadata":{"id":"_GdkcFxkpMri","colab_type":"code","colab":{}},"source":["def RPN_Layer(base_layer, num_anchors):\n","  '''\n","    base_layer: vgg16\n","    num_anchors: 9\n","  '''\n","  x = tf.keras.layers.Conv2D(512, (3, 3), padding='same', activation='relu', kernel_initializer='normal', name='rpn_conv1')(base_layer)\n","\n","  x_class = tf.keras.layers.Conv2D(num_anchors, (1, 1), activation='sigmoid', kernel_initializer='uniform', name='rpn_out_class')(x)\n","  x_regr = tf.keras.layers.Conv2D(num_anchors * 4, (1, 1), activation='linear', kernel_initializer='zero', name='rpn_out_regress')(x)\n","\n","  return [x_class, x_regr, base_layer]"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"6wPsInK9YHtI","colab_type":"text"},"source":["### RPN to Region Of Interest \n","The next layer is RPN to RoI layer that converts anchors into regions of interest, remove duplicate proposals (proposals that overlap each other by IoU greater than 0.7) using the non max suppression technique and pick bounding boxes upto a max number i.e. 300 (in our case)."]},{"cell_type":"code","metadata":{"id":"k6eXdj2bhq9x","colab_type":"code","colab":{}},"source":["def apply_regr_np(X, T):\n","    try:\n","        x = X[0, :, :]\n","        y = X[1, :, :]\n","        w = X[2, :, :]\n","        h = X[3, :, :]\n","\n","        tx = T[0, :, :]\n","        ty = T[1, :, :]\n","        tw = T[2, :, :]\n","        th = T[3, :, :]\n","\n","        cx = x + w/2.\n","        cy = y + h/2.\n","        cx1 = tx * w + cx\n","        cy1 = ty * h + cy\n","\n","        w1 = np.exp(tw.astype(np.float64)) * w\n","        h1 = np.exp(th.astype(np.float64)) * h\n","        x1 = cx1 - w1/2.\n","        y1 = cy1 - h1/2.\n","\n","        x1 = np.round(x1)\n","        y1 = np.round(y1)\n","        w1 = np.round(w1)\n","        h1 = np.round(h1)\n","        return np.stack([x1, y1, w1, h1])\n","    except Exception as e:\n","        print(e)\n","        return X"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"EYQEUbjvZyHx","colab_type":"code","colab":{}},"source":["def non_max_suppression_fast(boxes, probs, overlap_thresh=0.9, max_boxes=300):\n","    # code used from here: http://www.pyimagesearch.com/2015/02/16/faster-non-maximum-suppression-python/\n","    # if there are no boxes, return an empty list\n","\n","    # Process explanation:\n","    #   Step 1: Sort the probs list\n","    #   Step 2: Find the larget prob 'Last' in the list and save it to the pick list\n","    #   Step 3: Calculate the IoU with 'Last' box and other boxes in the list. If the IoU is larger than overlap_threshold, delete the box from list\n","    #   Step 4: Repeat step 2 and step 3 until there is no item in the probs list \n","    if len(boxes) == 0:\n","        return []\n","\n","    # grab the coordinates of the bounding boxes\n","    x1 = boxes[:, 0]\n","    y1 = boxes[:, 1]\n","    x2 = boxes[:, 2]\n","    y2 = boxes[:, 3]\n","\n","\n","    # if the bounding boxes integers, convert them to floats --\n","    # this is important since we'll be doing a bunch of divisions\n","    if boxes.dtype.kind == \"i\":\n","        boxes = boxes.astype(\"float\")\n","\n","    # initialize the list of picked indexes\t\n","    pick = []\n","\n","    # calculate the areas\n","    area = (x2 - x1) * (y2 - y1)\n","\n","    # sort the bounding boxes \n","    idxs = np.argsort(probs)\n","\n","    # keep looping while some indexes still remain in the indexes\n","    # list\n","    while len(idxs) > 0:\n","        # grab the last index in the indexes list and add the\n","        # index value to the list of picked indexes\n","        last = len(idxs) - 1\n","        i = idxs[last]\n","        pick.append(i)\n","\n","        # find the intersection\n","\n","        xx1_int = np.maximum(x1[i], x1[idxs[:last]])\n","        yy1_int = np.maximum(y1[i], y1[idxs[:last]])\n","        xx2_int = np.minimum(x2[i], x2[idxs[:last]])\n","        yy2_int = np.minimum(y2[i], y2[idxs[:last]])\n","\n","        ww_int = np.maximum(0, xx2_int - xx1_int)\n","        hh_int = np.maximum(0, yy2_int - yy1_int)\n","\n","        area_int = ww_int * hh_int\n","\n","        # find the union\n","        area_union = area[i] + area[idxs[:last]] - area_int\n","\n","        # compute the ratio of overlap\n","        overlap = area_int/(area_union + 1e-6)\n","\n","        # delete all indexes from the index list that have\n","        idxs = np.delete(idxs, np.concatenate(([last],\n","            np.where(overlap > overlap_thresh)[0])))\n","\n","        if len(pick) >= max_boxes:\n","            break\n","\n","    # return only the bounding boxes that were picked using the integer data type\n","    boxes = boxes[pick].astype(\"int\")\n","    probs = probs[pick]\n","    return boxes, probs"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"cuVGZsNEYIE2","colab_type":"code","colab":{}},"source":["# RPN to ROI\n","def rpn_to_roi(rpn_layer, regr_layer, dim_ordering, use_regr=True, max_boxes=10,overlap_thresh=0.9):\n","\t\n","\tregr_layer = regr_layer / 4.0\n","\n","\tanchor_sizes = [64, 128, 256]   # (3 in here)\n","\tanchor_ratios = [[1, 1], [1./math.sqrt(2), 2./math.sqrt(2)], [2./math.sqrt(2), 1./math.sqrt(2)]]  # (3 in here)\n","\n","\n","\t(rows, cols) = rpn_layer.shape[1:3]\n","\n","\tcurr_layer = 0\n","\n","\t# A.shape = (4, feature_map.height, feature_map.width, num_anchors) \n","\tA = np.zeros((4, rpn_layer.shape[1], rpn_layer.shape[2], rpn_layer.shape[3]))\n","\n","\tfor anchor_size in anchor_sizes:\n","\t\tfor anchor_ratio in anchor_ratios:\n","\t\t\t# anchor_x = (128 * 1) / 16 = 8  => width of current anchor\n","\t\t\t# anchor_y = (128 * 2) / 16 = 16 => height of current anchor\n","\t\t\tanchor_x = (anchor_size * anchor_ratio[0])/ 16\n","\t\t\tanchor_y = (anchor_size * anchor_ratio[1])/ 16\n","\t\t\t\n","\t\t\t# curr_layer: 0~8 (9 anchors)\n","\t\t\t# the Kth anchor of all position in the feature map (9th in total)\n","\t\t\tregr = regr_layer[0, :, :, 4 * curr_layer:4 * curr_layer + 4]\n","\t\t\tregr = np.transpose(regr, (2, 0, 1)) \n","\n","\t\t\t\n","\t\t\t# For every point in x, there are all the y points and vice versa\n","\t\t\tX, Y = np.meshgrid(np.arange(cols),np. arange(rows))\n","\n","\t\t\t# Calculate anchor position and size for each feature map point\n","\t\t\tA[0, :, :, curr_layer] = X - anchor_x/2 # Top left x coordinate\n","\t\t\tA[1, :, :, curr_layer] = Y - anchor_y/2 # Top left y coordinate\n","\t\t\tA[2, :, :, curr_layer] = anchor_x       # width of current anchor\n","\t\t\tA[3, :, :, curr_layer] = anchor_y       # height of current anchor\n","\n","\t\t\t# Apply regression to x, y, w and h if there is rpn regression layer\n","\t\t\tif use_regr:\n","\t\t\t\tA[:, :, :, curr_layer] = apply_regr_np(A[:, :, :, curr_layer], regr)\n","\n","\t\t\t# Avoid width and height exceeding 1\n","\t\t\tA[2, :, :, curr_layer] = np.maximum(1, A[2, :, :, curr_layer])\n","\t\t\tA[3, :, :, curr_layer] = np.maximum(1, A[3, :, :, curr_layer])\n","\n","\t\t\t# Convert (x, y , w, h) to (x1, y1, x2, y2)\n","\t\t\t# x1, y1 is top left coordinate\n","\t\t\t# x2, y2 is bottom right coordinate\n","\t\t\tA[2, :, :, curr_layer] += A[0, :, :, curr_layer]\n","\t\t\tA[3, :, :, curr_layer] += A[1, :, :, curr_layer]\n","\n","\t\t\t# Avoid bboxes drawn outside the feature map\n","\t\t\tA[0, :, :, curr_layer] = np.maximum(0, A[0, :, :, curr_layer])\n","\t\t\tA[1, :, :, curr_layer] = np.maximum(0, A[1, :, :, curr_layer])\n","\t\t\tA[2, :, :, curr_layer] = np.minimum(cols-1, A[2, :, :, curr_layer])\n","\t\t\tA[3, :, :, curr_layer] = np.minimum(rows-1, A[3, :, :, curr_layer])\n","\n","\t\t\tcurr_layer += 1\n","\n","\tall_boxes = np.reshape(A.transpose((0, 3, 1, 2)), (4, -1)).transpose((1, 0))  \n","\tall_probs = np.transpose(rpn_layer,(0, 3, 1, 2)).reshape((-1))                  \n","\n","\tx1 = all_boxes[:, 0]\n","\ty1 = all_boxes[:, 1]\n","\tx2 = all_boxes[:, 2]\n","\ty2 = all_boxes[:, 3]\n","\n","\t# Find out the bboxes which is illegal and delete them from bboxes list\n","\tidxs = np.where((x1 - x2 >= 0) | (y1 - y2 >= 0))\n","\n","\tall_boxes = np.delete(all_boxes, idxs, 0)\n","\tall_probs = np.delete(all_probs, idxs, 0)\n","\n","\t# Apply non_max_suppression\n","\t# Only extract the bboxes. Don't need rpn probs in the later process\n","\tresult = non_max_suppression_fast(all_boxes, all_probs, overlap_thresh=overlap_thresh, max_boxes=max_boxes)[0]\n","\n","\treturn result"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"gNHg4oNqNO77","colab_type":"text"},"source":["### Filter Regions Of Interest\n","Now for the final layer of the model, using the regions or bounding boxes picked in the last layer, we can now filter those regions that have an overlap greater than 0.65 with the ground truth boxes. However, for calculating the accuracy metric (intersection of union) we involve only those regions that have an overlap greater than 0.3 with the ground truth boxes."]},{"cell_type":"code","metadata":{"id":"nNdqiREdNMhd","colab_type":"code","colab":{}},"source":["def filter_roi(labels, Y_pred, thresh= 0.3):\n","  filter_labels = []\n","  iou_acc = []\n","  for label in labels:\n","    for predicted in Y_pred:\n","      label_iou = iou(label, predicted)\n","      if label_iou > thresh:\n","        iou_acc.append(label_iou)\n","        filter_labels.append(predicted)\n","  \n","  if iou_acc == []:\n","    mean = 0\n","  else:\n","    mean = np.mean(np.asarray(iou_acc))\n","  \n","  return filter_labels, mean "],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"PKxUmClzwVb8","colab_type":"text"},"source":["## Define Classification and Regression Loss\n"]},{"cell_type":"code","metadata":{"id":"BEnbLCKZWdLj","colab_type":"code","colab":{}},"source":["epsilon = 1e-4"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"iwg9tCFSSyQD","colab_type":"code","colab":{}},"source":["# RPN Loss Regression\n","def rpn_loss_regr(num_anchors):\n","\n","    def rpn_loss_regr_fixed_num(y_true, y_pred):\n","\n","        # x is the difference between true value and predicted vaue\n","        x = y_true[:, :, :, 4 * num_anchors:] - y_pred\n","\n","        # absolute value of x\n","        x_abs = K.abs(x)\n","\n","        # If x_abs <= 1.0, x_bool = 1\n","        x_bool = K.cast(K.less_equal(x_abs, 1.0), tf.float32)\n","\n","        return 1.0 * K.sum(\n","            y_true[:, :, :, :4 * num_anchors] * (x_bool * (0.5 * x * x) + (1 - x_bool) * (x_abs - 0.5))) / K.sum(epsilon + y_true[:, :, :, :4 * num_anchors])\n","\n","    return rpn_loss_regr_fixed_num"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"dmBFpG2LSyca","colab_type":"code","colab":{}},"source":["# RPN Loss Classification\n","def rpn_loss_cls(num_anchors):\n"," \n","    def rpn_loss_cls_fixed_num(y_true, y_pred):\n","\n","            return 1.0 * K.sum(y_true[:, :, :, :num_anchors] * K.binary_crossentropy(y_pred[:, :, :, :], y_true[:, :, :, num_anchors:])) / K.sum(epsilon + y_true[:, :, :, :num_anchors])\n","\n","    return rpn_loss_cls_fixed_num\n"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"8Rtp_lihSzdN","colab_type":"text"},"source":["## Model Initialization"]},{"cell_type":"code","metadata":{"id":"2xCm5oMLhQOQ","colab_type":"code","outputId":"f6dbd664-4dde-4f45-a78d-16f6c31a2e21","executionInfo":{"status":"ok","timestamp":1562743335725,"user_tz":-300,"elapsed":1197,"user":{"displayName":"Maliha Sameen","photoUrl":"","userId":"02324899299454165728"}},"colab":{"base_uri":"https://localhost:8080/","height":323}},"source":["input_image = tf.keras.layers.Input(shape=(None,None,3))\n","# VGG16 gives feature map\n","vgg16 = base_model(input_image)\n","# RPN Layer x_regr gives mean distance between anchors\n","rpn = RPN_Layer(vgg16,9)\n","\n","\n","model_rpn = tf.keras.models.Model(input_image, rpn[:2])\n","model_rpn.compile(optimizer='adam', loss=[rpn_loss_cls(9), rpn_loss_regr(9)], metrics = [\"acc\"])\n","model_rpn.summary()"],"execution_count":29,"outputs":[{"output_type":"stream","text":["Model: \"model_2\"\n","__________________________________________________________________________________________________\n","Layer (type)                    Output Shape         Param #     Connected to                     \n","==================================================================================================\n","input_3 (InputLayer)            [(None, None, None,  0                                            \n","__________________________________________________________________________________________________\n","model (Model)                   multiple             14714688    input_3[0][0]                    \n","__________________________________________________________________________________________________\n","rpn_conv1 (Conv2D)              (None, None, None, 5 2359808     model[2][0]                      \n","__________________________________________________________________________________________________\n","rpn_out_class (Conv2D)          (None, None, None, 9 4617        rpn_conv1[0][0]                  \n","__________________________________________________________________________________________________\n","rpn_out_regress (Conv2D)        (None, None, None, 3 18468       rpn_conv1[0][0]                  \n","==================================================================================================\n","Total params: 17,097,581\n","Trainable params: 17,097,581\n","Non-trainable params: 0\n","__________________________________________________________________________________________________\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"fWvxfTYcmEIh","colab_type":"text"},"source":["## Training"]},{"cell_type":"code","metadata":{"id":"svIk-szNmCRl","colab_type":"code","colab":{}},"source":["num_epochs = 50\n","epoch_num=0\n","losses = np.zeros((338, 3))\n","loss_cls = []\n","loss_regr = []\n","accuracy = []"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"colab_type":"code","outputId":"b78740ec-31e6-4c8c-840e-05627c6a7a8f","executionInfo":{"status":"ok","timestamp":1562746356608,"user_tz":-300,"elapsed":3014457,"user":{"displayName":"Maliha Sameen","photoUrl":"","userId":"02324899299454165728"}},"id":"vQr3_OWyFTYg","colab":{"base_uri":"https://localhost:8080/","height":1000}},"source":["for epoch_num in range(num_epochs):\n","  progbar = Progbar(338)\n","  print('Epoch {}/{}'.format(epoch_num + 1, num_epochs))\n","  \n","  data_gen_train = get_anchor_gt(train_images, train_labels)\n","  iter_num = 0\n","  while True:\n","    try:\n","      \n","      # Generate X (x_img) and label Y ([y_rpn_cls, y_rpn_regr])\n","      X, Y, labels, index = next(data_gen_train)\n","\n","      # Train rpn model and get loss value [_, loss_rpn_cls, loss_rpn_regr]\n","      loss_rpn = model_rpn.train_on_batch(X, Y)\n","\n","      # Get predicted rpn from rpn model [rpn_cls, rpn_regr]\n","      P_rpn = model_rpn.predict_on_batch(X)\n","      R_pred = rpn_to_roi(P_rpn[0], P_rpn[1],image_dim_ordering(), use_regr=True, overlap_thresh=0.7, max_boxes=300)\n","      R_pred = R_pred * 16\n","      pred_labels, acc = filter_roi(labels, R_pred)\n","\n","      losses[iter_num, 0] = loss_rpn[1]\n","      losses[iter_num, 1] = loss_rpn[2]\n","      losses[iter_num, 2] = acc\n","\n","      iter_num += 1\n","\n","      progbar.update(iter_num, [('rpn_cls', np.mean(losses[:iter_num, 0])), ('rpn_regr', np.mean(losses[:iter_num, 1])),('acc', np.mean(losses[:iter_num, 2]))])\n","      \n","      if iter_num == 338:\n","        loss_rpn_cls = np.mean(losses[:, 0])\n","        loss_rpn_regr = np.mean(losses[:, 1])\n","        mean_iou = np.mean(losses[:, 2])\n","        loss_cls.append(loss_rpn_cls)\n","        loss_regr.append(loss_rpn_regr)\n","        accuracy.append(mean_iou)\n","        \n","        print('Loss RPN Classifier: ', loss_rpn_cls)\n","        print('Loss RPN Regressor: ', loss_rpn_regr)\n","        print('Mean IOU: ', mean_iou)\n","\n","        break\n","\n","    except Exception as e:\n","      print('Exception: {}'.format(e))\n","      continue\n","      \n","print(\"Training complete!\")"],"execution_count":31,"outputs":[{"output_type":"stream","text":["Epoch 1/50\n","338/338 [==============================] - 63s 187ms/step - rpn_cls: 0.9582 - rpn_regr: 0.8874 - acc: 0.3892\n","Loss RPN Classifier:  1.0589410135167008\n","Loss RPN Regressor:  1.0413739020473285\n","Mean IOU:  0.3797976924577539\n","Epoch 2/50\n","338/338 [==============================] - 61s 179ms/step - rpn_cls: 0.6819 - rpn_regr: 0.6334 - acc: 0.4039\n","Loss RPN Classifier:  0.8484327098834323\n","Loss RPN Regressor:  0.2308422221647873\n","Mean IOU:  0.382865956207247\n","Epoch 3/50\n","338/338 [==============================] - 61s 180ms/step - rpn_cls: 0.8918 - rpn_regr: 0.0516 - acc: 0.3269\n","Loss RPN Classifier:  0.9667005688527055\n","Loss RPN Regressor:  0.11996461542739296\n","Mean IOU:  0.34334124762644197\n","Epoch 4/50\n","338/338 [==============================] - 60s 179ms/step - rpn_cls: 0.8652 - rpn_regr: 0.0709 - acc: 0.3426\n","Loss RPN Classifier:  0.9724161463439449\n","Loss RPN Regressor:  0.07092860077381045\n","Mean IOU:  0.38773156904256834\n","Epoch 5/50\n","338/338 [==============================] - 61s 180ms/step - rpn_cls: 0.7878 - rpn_regr: 0.1209 - acc: 0.3891\n","Loss RPN Classifier:  1.0375003279465822\n","Loss RPN Regressor:  0.15317107762138432\n","Mean IOU:  0.3880698484368616\n","Epoch 6/50\n"," 12/338 [>.............................] - ETA: 55s - rpn_cls: 1.0659 - rpn_regr: 0.0269 - acc: 0.4167"],"name":"stdout"},{"output_type":"stream","text":["/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:19: RuntimeWarning: overflow encountered in exp\n","/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:19: RuntimeWarning: overflow encountered in multiply\n","/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:50: RuntimeWarning: invalid value encountered in add\n","/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:18: RuntimeWarning: overflow encountered in exp\n","/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:49: RuntimeWarning: invalid value encountered in add\n","/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:69: RuntimeWarning: invalid value encountered in greater_equal\n","/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:63: RuntimeWarning: invalid value encountered in greater\n"],"name":"stderr"},{"output_type":"stream","text":[" 14/338 [>.............................] - ETA: 54s - rpn_cls: 1.0611 - rpn_regr: 0.0266 - acc: 0.4160"],"name":"stdout"},{"output_type":"stream","text":["/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:18: RuntimeWarning: overflow encountered in multiply\n"],"name":"stderr"},{"output_type":"stream","text":["338/338 [==============================] - 60s 178ms/step - rpn_cls: 0.9175 - rpn_regr: 134.3152 - acc: 0.3973\n","Loss RPN Classifier:  1.019102559301448\n","Loss RPN Regressor:  47.20891486425587\n","Mean IOU:  0.3881589292596427\n","Epoch 7/50\n","338/338 [==============================] - 61s 180ms/step - rpn_cls: 0.8476 - rpn_regr: 0.1826 - acc: 0.3610\n","Loss RPN Classifier:  0.9845978418377951\n","Loss RPN Regressor:  0.15037627608829437\n","Mean IOU:  0.34578932460797657\n","Epoch 8/50\n","338/338 [==============================] - 61s 181ms/step - rpn_cls: 0.6842 - rpn_regr: 0.2147 - acc: 0.3151\n","Loss RPN Classifier:  0.9007604610209352\n","Loss RPN Regressor:  0.18726414018558593\n","Mean IOU:  0.33781311922474705\n","Epoch 9/50\n","338/338 [==============================] - 61s 181ms/step - rpn_cls: 0.7386 - rpn_regr: 0.0988 - acc: 0.3270\n","Loss RPN Classifier:  1.0566342531947022\n","Loss RPN Regressor:  0.09539388959466882\n","Mean IOU:  0.3619603301566493\n","Epoch 10/50\n","338/338 [==============================] - 61s 180ms/step - rpn_cls: 0.7956 - rpn_regr: 0.0920 - acc: 0.4180\n","Loss RPN Classifier:  1.0495201766888491\n","Loss RPN Regressor:  0.1488749769036851\n","Mean IOU:  0.4044426896619677\n","Epoch 11/50\n","338/338 [==============================] - 61s 179ms/step - rpn_cls: 0.7025 - rpn_regr: 0.0998 - acc: 0.3591\n","Loss RPN Classifier:  0.9290598389928234\n","Loss RPN Regressor:  0.09789482353133916\n","Mean IOU:  0.37787352574290456\n","Epoch 12/50\n","338/338 [==============================] - 61s 179ms/step - rpn_cls: 0.7019 - rpn_regr: 0.0448 - acc: 0.3902\n","Loss RPN Classifier:  0.8802739371771943\n","Loss RPN Regressor:  0.046012175776417086\n","Mean IOU:  0.4057290566403134\n","Epoch 13/50\n","338/338 [==============================] - 61s 179ms/step - rpn_cls: 0.6340 - rpn_regr: 0.0244 - acc: 0.4099\n","Loss RPN Classifier:  0.9125017259959145\n","Loss RPN Regressor:  0.027173994613271316\n","Mean IOU:  0.40929368135123073\n","Epoch 14/50\n","338/338 [==============================] - 60s 179ms/step - rpn_cls: 0.5430 - rpn_regr: 0.0216 - acc: 0.4112\n","Loss RPN Classifier:  0.7572354287861952\n","Loss RPN Regressor:  0.026314582305095164\n","Mean IOU:  0.40949605346263407\n","Epoch 15/50\n","338/338 [==============================] - 61s 180ms/step - rpn_cls: 0.7167 - rpn_regr: 0.0286 - acc: 0.3943\n","Loss RPN Classifier:  0.9742542929119966\n","Loss RPN Regressor:  0.0405352109789672\n","Mean IOU:  0.3857049727221738\n","Epoch 16/50\n","338/338 [==============================] - 61s 180ms/step - rpn_cls: 0.6289 - rpn_regr: 0.0286 - acc: 0.3947\n","Loss RPN Classifier:  0.8541407908292892\n","Loss RPN Regressor:  0.0394191622724426\n","Mean IOU:  0.40209110386052604\n","Epoch 17/50\n","338/338 [==============================] - 61s 181ms/step - rpn_cls: 0.9104 - rpn_regr: 0.0472 - acc: 0.3669\n","Loss RPN Classifier:  1.040576512040846\n","Loss RPN Regressor:  0.03871526455054798\n","Mean IOU:  0.38514491114875044\n","Epoch 18/50\n","338/338 [==============================] - 61s 181ms/step - rpn_cls: 0.7967 - rpn_regr: 0.0669 - acc: 0.3415\n","Loss RPN Classifier:  0.9426130011921088\n","Loss RPN Regressor:  0.09732841220326148\n","Mean IOU:  0.3017528708326396\n","Epoch 19/50\n","338/338 [==============================] - 60s 178ms/step - rpn_cls: 0.6598 - rpn_regr: 0.0903 - acc: 0.3814\n","Loss RPN Classifier:  0.8825972805377061\n","Loss RPN Regressor:  0.06793722849750489\n","Mean IOU:  0.4085883392413426\n","Epoch 20/50\n","338/338 [==============================] - 60s 177ms/step - rpn_cls: 0.6460 - rpn_regr: 0.0258 - acc: 0.4255\n","Loss RPN Classifier:  0.7675705722930287\n","Loss RPN Regressor:  0.031974011189690045\n","Mean IOU:  0.4234803063199741\n","Epoch 21/50\n","338/338 [==============================] - 59s 176ms/step - rpn_cls: 0.3869 - rpn_regr: 0.0233 - acc: 0.4262\n","Loss RPN Classifier:  0.5544312766854054\n","Loss RPN Regressor:  0.02589314836476924\n","Mean IOU:  0.4244277175231299\n","Epoch 22/50\n","338/338 [==============================] - 60s 176ms/step - rpn_cls: 0.5955 - rpn_regr: 0.0222 - acc: 0.4267\n","Loss RPN Classifier:  0.7462390839546221\n","Loss RPN Regressor:  0.024881408075917755\n","Mean IOU:  0.4273544996403601\n","Epoch 23/50\n","338/338 [==============================] - 60s 176ms/step - rpn_cls: 0.4398 - rpn_regr: 0.0223 - acc: 0.4270\n","Loss RPN Classifier:  0.590472064052809\n","Loss RPN Regressor:  0.025203741836318877\n","Mean IOU:  0.42523085272720096\n","Epoch 24/50\n","338/338 [==============================] - 60s 176ms/step - rpn_cls: 0.4801 - rpn_regr: 0.0225 - acc: 0.4249\n","Loss RPN Classifier:  0.6559591352552645\n","Loss RPN Regressor:  0.02524876445244251\n","Mean IOU:  0.4265741073497744\n","Epoch 25/50\n","338/338 [==============================] - 60s 177ms/step - rpn_cls: 0.5273 - rpn_regr: 0.0224 - acc: 0.4270\n","Loss RPN Classifier:  0.7207992569330395\n","Loss RPN Regressor:  0.025162670869582068\n","Mean IOU:  0.4286817524364543\n","Epoch 26/50\n","338/338 [==============================] - 60s 176ms/step - rpn_cls: 0.6128 - rpn_regr: 0.0224 - acc: 0.4280\n","Loss RPN Classifier:  0.7974393978711843\n","Loss RPN Regressor:  0.02499359653685407\n","Mean IOU:  0.42826629572707375\n","Epoch 27/50\n","338/338 [==============================] - 60s 178ms/step - rpn_cls: 0.6301 - rpn_regr: 0.0223 - acc: 0.4267\n","Loss RPN Classifier:  0.8168034219601847\n","Loss RPN Regressor:  0.02507411182262885\n","Mean IOU:  0.42765768568046925\n","Epoch 28/50\n","338/338 [==============================] - 60s 177ms/step - rpn_cls: 0.5222 - rpn_regr: 0.0236 - acc: 0.4232\n","Loss RPN Classifier:  0.7410851533458562\n","Loss RPN Regressor:  0.032653722362372385\n","Mean IOU:  0.4181605028291145\n","Epoch 29/50\n","338/338 [==============================] - 60s 179ms/step - rpn_cls: 0.5786 - rpn_regr: 0.0779 - acc: 0.4158\n","Loss RPN Classifier:  0.8375914900120676\n","Loss RPN Regressor:  0.09645692090443253\n","Mean IOU:  0.4103272689141618\n","Epoch 30/50\n","338/338 [==============================] - 60s 177ms/step - rpn_cls: 0.5677 - rpn_regr: 0.0311 - acc: 0.4268\n","Loss RPN Classifier:  0.6918763791444227\n","Loss RPN Regressor:  0.03426542783365447\n","Mean IOU:  0.42740651743647956\n","Epoch 31/50\n","338/338 [==============================] - 60s 177ms/step - rpn_cls: 0.6193 - rpn_regr: 0.0238 - acc: 0.4270\n","Loss RPN Classifier:  0.740926001036976\n","Loss RPN Regressor:  0.02742644439254386\n","Mean IOU:  0.42585383141175726\n","Epoch 32/50\n","338/338 [==============================] - 60s 177ms/step - rpn_cls: 0.5021 - rpn_regr: 0.0226 - acc: 0.4248\n","Loss RPN Classifier:  0.6742910080751199\n","Loss RPN Regressor:  0.025484084765448858\n","Mean IOU:  0.4250712500490837\n","Epoch 33/50\n","338/338 [==============================] - 60s 176ms/step - rpn_cls: 0.6124 - rpn_regr: 0.0220 - acc: 0.4262\n","Loss RPN Classifier:  0.8377834704421552\n","Loss RPN Regressor:  0.024560088784979468\n","Mean IOU:  0.4255577870454818\n","Epoch 34/50\n","338/338 [==============================] - 59s 176ms/step - rpn_cls: 0.4350 - rpn_regr: 0.0214 - acc: 0.4268\n","Loss RPN Classifier:  0.7305436055182849\n","Loss RPN Regressor:  0.024113431140317543\n","Mean IOU:  0.426566927504145\n","Epoch 35/50\n","338/338 [==============================] - 60s 177ms/step - rpn_cls: 0.6150 - rpn_regr: 0.0223 - acc: 0.4255\n","Loss RPN Classifier:  0.7266532709127097\n","Loss RPN Regressor:  0.025106796387930196\n","Mean IOU:  0.42487538099200045\n","Epoch 36/50\n","338/338 [==============================] - 59s 175ms/step - rpn_cls: 0.5577 - rpn_regr: 0.0218 - acc: 0.4246\n","Loss RPN Classifier:  0.715962073844622\n","Loss RPN Regressor:  0.024145938491189754\n","Mean IOU:  0.4263585365889984\n","Epoch 37/50\n","338/338 [==============================] - 59s 175ms/step - rpn_cls: 0.5400 - rpn_regr: 1.8359 - acc: 0.4239\n","Loss RPN Classifier:  0.8591614993382194\n","Loss RPN Regressor:  9.724051083902381\n","Mean IOU:  0.4214789615826223\n","Epoch 38/50\n","338/338 [==============================] - 59s 174ms/step - rpn_cls: 0.9000 - rpn_regr: 0.8361 - acc: 0.4117\n","Loss RPN Classifier:  0.9224891282184055\n","Loss RPN Regressor:  0.3924346568073923\n","Mean IOU:  0.4105455991095196\n","Epoch 39/50\n","338/338 [==============================] - 60s 177ms/step - rpn_cls: 0.5766 - rpn_regr: 0.0358 - acc: 0.4201\n","Loss RPN Classifier:  0.7722787812981103\n","Loss RPN Regressor:  0.05185978218207698\n","Mean IOU:  0.41551322735214996\n","Epoch 40/50\n","338/338 [==============================] - 61s 179ms/step - rpn_cls: 0.7668 - rpn_regr: 0.0517 - acc: 0.4102\n","Loss RPN Classifier:  1.0558644601881375\n","Loss RPN Regressor:  0.0683185291647382\n","Mean IOU:  0.39748597300626165\n","Epoch 41/50\n","338/338 [==============================] - 61s 181ms/step - rpn_cls: 0.9385 - rpn_regr: 0.0272 - acc: 0.3703\n","Loss RPN Classifier:  1.0581519499007122\n","Loss RPN Regressor:  0.03132478419220152\n","Mean IOU:  0.3835968458084306\n","Epoch 42/50\n","338/338 [==============================] - 61s 181ms/step - rpn_cls: 0.9550 - rpn_regr: 0.0245 - acc: 0.3857\n","Loss RPN Classifier:  1.0536559207258833\n","Loss RPN Regressor:  0.027853357311498\n","Mean IOU:  0.390910728212979\n","Epoch 43/50\n","338/338 [==============================] - 61s 181ms/step - rpn_cls: 0.7744 - rpn_regr: 0.0236 - acc: 0.3835\n","Loss RPN Classifier:  0.9375136380896958\n","Loss RPN Regressor:  0.02683285191261138\n","Mean IOU:  0.3897595842535307\n","Epoch 44/50\n","338/338 [==============================] - 61s 180ms/step - rpn_cls: 1.0000 - rpn_regr: 0.0228 - acc: 0.3850\n","Loss RPN Classifier:  1.112045561622335\n","Loss RPN Regressor:  0.026525605979689656\n","Mean IOU:  0.39198911496178435\n","Epoch 45/50\n","338/338 [==============================] - 60s 178ms/step - rpn_cls: 0.8970 - rpn_regr: 0.0964 - acc: 0.3917\n","Loss RPN Classifier:  1.105812864147321\n","Loss RPN Regressor:  0.1664422452312029\n","Mean IOU:  0.3940818167222521\n","Epoch 46/50\n","338/338 [==============================] - 59s 176ms/step - rpn_cls: 0.7327 - rpn_regr: 0.5351 - acc: 0.3929\n","Loss RPN Classifier:  0.8669607599128241\n","Loss RPN Regressor:  0.41283553126736505\n","Mean IOU:  0.39503866838918295\n","Epoch 47/50\n","338/338 [==============================] - 59s 175ms/step - rpn_cls: 0.5899 - rpn_regr: 0.1147 - acc: 0.3957\n","Loss RPN Classifier:  0.8586321031256481\n","Loss RPN Regressor:  0.2619501713404333\n","Mean IOU:  0.40571282287692495\n","Epoch 48/50\n","338/338 [==============================] - 60s 176ms/step - rpn_cls: 0.6626 - rpn_regr: 0.0976 - acc: 0.4091\n","Loss RPN Classifier:  0.7999624891699588\n","Loss RPN Regressor:  0.0882852717554858\n","Mean IOU:  0.40480269205123615\n","Epoch 49/50\n","338/338 [==============================] - 60s 178ms/step - rpn_cls: 0.7126 - rpn_regr: 0.0512 - acc: 0.4071\n","Loss RPN Classifier:  0.8291470499877529\n","Loss RPN Regressor:  0.04732087538760161\n","Mean IOU:  0.40580925499019577\n","Epoch 50/50\n","338/338 [==============================] - 60s 178ms/step - rpn_cls: 0.6105 - rpn_regr: 0.0234 - acc: 0.4010\n","Loss RPN Classifier:  0.8210720819832644\n","Loss RPN Regressor:  0.032170732872086695\n","Mean IOU:  0.4030559375124502\n","Training complete!\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"rHKXM1czeofA","colab_type":"text"},"source":["## Training and Testing Accuracy"]},{"cell_type":"code","metadata":{"id":"rb_cE9EAdjUk","colab_type":"code","colab":{}},"source":["def calculate_accuracy(images, labels, losses):\n","  progbar = Progbar(338)\n","  print('Epoch 1/1')\n","  \n","  data_gen = get_anchor_gt(images, labels)\n","  iter_num = 0\n","  \n","  while True:\n","    try:\n","      \n","      # Generate X (x_img) and label Y ([y_rpn_cls, y_rpn_regr])\n","      X, Y, labels, index = next(data_gen)\n","\n","      # Train rpn model and get loss value [_, loss_rpn_cls, loss_rpn_regr]\n","      loss_rpn = model_rpn.train_on_batch(X, Y)\n","\n","      # Get predicted rpn from rpn model [rpn_cls, rpn_regr]\n","      P_rpn = model_rpn.predict_on_batch(X)\n","      R_pred = rpn_to_roi(P_rpn[0], P_rpn[1],image_dim_ordering(), use_regr=True, overlap_thresh=0.7, max_boxes=300)\n","      R_pred = R_pred * 16\n","      pred_labels, acc = filter_roi(labels, R_pred)\n","\n","      losses[iter_num, 0] = loss_rpn[1]\n","      losses[iter_num, 1] = loss_rpn[2]\n","      losses[iter_num, 2] = acc\n","\n","      iter_num += 1\n","\n","      progbar.update(iter_num, [('rpn_cls', np.mean(losses[:iter_num, 0])), ('rpn_regr', np.mean(losses[:iter_num, 1])),('acc', np.mean(losses[:iter_num, 2]))])\n","      \n","      if iter_num == 338:\n","        return np.mean(losses[:, 1]), np.mean(losses[:, 2])\n","        break\n","\n","    except Exception as e:\n","      continue"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"QEpnLtg5gWtU","colab_type":"code","outputId":"d22fab96-c40c-4acd-b612-4b7bd0bc5c0a","executionInfo":{"status":"ok","timestamp":1562750673953,"user_tz":-300,"elapsed":126075,"user":{"displayName":"Maliha Sameen","photoUrl":"","userId":"02324899299454165728"}},"colab":{"base_uri":"https://localhost:8080/","height":119}},"source":["train_losses = np.zeros((338, 3))\n","test_losses = np.zeros((338, 3))\n","\n","_, train_acc = calculate_accuracy(train_images, train_labels, train_losses)\n","print(\"Training Accuracy: \", train_acc * 100)\n","\n","_, test_acc = calculate_accuracy(test_images, test_labels, test_losses)\n","print(\"Testing Accuracy: \", test_acc * 100)"],"execution_count":126,"outputs":[{"output_type":"stream","text":["Epoch 1/1\n","338/338 [==============================] - 62s 185ms/step - rpn_cls: 0.8058 - rpn_regr: 0.0456 - acc: 0.4055\n","Training Accuracy:  40.484135239963095\n","Epoch 1/1\n","338/338 [==============================] - 62s 184ms/step - rpn_cls: 1.5612 - rpn_regr: 0.0720 - acc: 0.4188\n","Testing Accuracy:  41.91102007270898\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"WAA25YSCciiX","colab_type":"text"},"source":["## Loss Curves"]},{"cell_type":"code","metadata":{"id":"MtdVmrkGcnuc","colab_type":"code","outputId":"3bd48dcc-b09c-4930-9e44-70135a179bae","executionInfo":{"status":"ok","timestamp":1562754181794,"user_tz":-300,"elapsed":1558,"user":{"displayName":"Maliha Sameen","photoUrl":"","userId":"02324899299454165728"}},"colab":{"base_uri":"https://localhost:8080/","height":295}},"source":["# Classification Loss\n","plt.plot(loss_cls)   \n","plt.title('Training Classification Loss')  \n","plt.ylabel('Loss')  \n","plt.xlabel('Epoch')  \n","plt.legend(['train']) \n","plt.show()"],"execution_count":295,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAYUAAAEWCAYAAACJ0YulAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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rByfxm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\nVZiNJ9h/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amqWkpVqiogd+CzwaqAfeFpE7lNKvZxyzFbgj4GrlVJjItJRqvVoagerKZ5S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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"FDOkrEH2d95w","colab_type":"code","outputId":"6ba8ce35-449a-444a-9f2a-6b95b497b283","executionInfo":{"status":"ok","timestamp":1562754190390,"user_tz":-300,"elapsed":1548,"user":{"displayName":"Maliha Sameen","photoUrl":"","userId":"02324899299454165728"}},"colab":{"base_uri":"https://localhost:8080/","height":295}},"source":["# Regression Loss\n","plt.plot(loss_regr)   \n","plt.title('Training Regression Loss')  \n","plt.ylabel('Loss')  \n","plt.xlabel('Epoch')  \n","plt.legend(['train']) \n","plt.show()"],"execution_count":296,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAYIAAAEWCAYAAABrDZDcAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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KxSo1a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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"markdown","metadata":{"id":"ZHVneRi-kikp","colab_type":"text"},"source":["## Accuracy Curves"]},{"cell_type":"code","metadata":{"id":"Ho1Lhkd-jUXV","colab_type":"code","outputId":"db0b80f1-088f-4e91-ae9f-f85087a217f8","executionInfo":{"status":"ok","timestamp":1562754197625,"user_tz":-300,"elapsed":1484,"user":{"displayName":"Maliha Sameen","photoUrl":"","userId":"02324899299454165728"}},"colab":{"base_uri":"https://localhost:8080/","height":295}},"source":["# Regression Loss\n","plt.plot(accuracy)   \n","plt.title('Training Accuracy')  \n","plt.ylabel('Accuracy')  \n","plt.xlabel('Epoch')  \n","plt.legend(['train']) \n","plt.show()"],"execution_count":297,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAYsAAAEWCAYAAACXGLsWAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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npjDUEOlPiYaLIoSGyxKC3MzstcQj/H3LDJrGM2YbBVPzqIceL+qnlRRT1VDIvKe5DQrc9QU58fc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\nTEz/PxWOStz/WnvTAAAAAElFTkSuQmCC\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"markdown","metadata":{"id":"N0Z_VCJMf7Ib","colab_type":"text"},"source":["## Testing"]},{"cell_type":"code","metadata":{"id":"ayM3g9FNkoRO","colab_type":"code","colab":{}},"source":["def predict_labels(data_gen):\n","  X, Y, labels, index = next(data_gen)\n","  P_rpn = model_rpn.predict_on_batch(X)\n","  R_pred = rpn_to_roi(P_rpn[0], P_rpn[1],image_dim_ordering(), use_regr=True, overlap_thresh=0.4, max_boxes=300)\n","  R_pred = R_pred * 16\n","  pred_labels, acc = filter_roi(labels, R_pred, 0.6)\n","  \n","  return pred_labels, index\n","\n","def inference(data_gen, images):\n","  rows = 2\n","  columns = 3\n","  fig=plt.figure(figsize=(16, 12))\n","  for i in range(6):\n","    fig.add_subplot(rows, columns, i+1, xticks=[], yticks=[])\n","    labels, index = predict_labels(data_gen)\n","    image = images[index]\n","    for label in labels:\n","      cv2.rectangle(image,(label[0], label[1]),(label[2],label[3]),(255, 0, 0))\n","    plt.imshow(image)\n","  plt.show()"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"IE96JCauDmXV","colab_type":"code","colab":{}},"source":["data_gen_train = get_anchor_gt(train_images, train_labels)\n","data_gen_test = get_anchor_gt(test_images, test_labels)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"JrmpBviDmqvp","colab_type":"code","outputId":"0e8a97c7-6a25-486f-bff3-7f4d3bbc760c","executionInfo":{"status":"ok","timestamp":1562753621211,"user_tz":-300,"elapsed":2869,"user":{"displayName":"Maliha Sameen","photoUrl":"","userId":"02324899299454165728"}},"colab":{"base_uri":"https://localhost:8080/","height":656}},"source":["inference(data_gen_train, np.copy(train_images))"],"execution_count":275,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAA5IAAAJ/CAYAAAAK3Oz6AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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O1sp2rOByXfuHGjbsfMb7W9a9cux3+AmjHV7FO5bQBq23c3Zhm17W77myJ+bWDfvn19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\ndcL1zz33XML1APLb8OHDm9xuJts+6j6iPrO8vLxcS5cuVbdu3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v373//Wzp07c+6koLS0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\nAElFTkSuQmCC\n","text/plain":["<Figure size 1152x864 with 6 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"GVxe6dQUm7bD","colab_type":"code","outputId":"2226f356-b7ef-4db7-9b6b-05ac7a929e9e","executionInfo":{"status":"ok","timestamp":1562753940842,"user_tz":-300,"elapsed":3712,"user":{"displayName":"Maliha Sameen","photoUrl":"","userId":"02324899299454165728"}},"colab":{"base_uri":"https://localhost:8080/","height":656}},"source":["inference(data_gen_test, np.copy(test_images))"],"execution_count":294,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAA5IAAAJ/CAYAAAAK3Oz6AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nDMM0LLrRar7WyzR/eNZWhmHqBc9kyDBMW4DrOoZh2go8ayvDMAzjFt2Q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1nXG62ssj4jU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iIaNeuXbZG55VXXmk6/t3vfkdjxoyx2fjf//7XZoMO2XmStG/fXoVvuOEGU5osw1WD+Pzzz6dP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size 1152x864 with 6 Axes>"]},"metadata":{"tags":[]}}]}]}